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Environmental performance assessment of Napier grass for bioenergy production
Nimmanterdwong, Prathana ; Chalermsinsuwan, Benjapon; Østergård, Hanne; Piumsomboon, Pornpote
Published in:Journal of Cleaner Production
Link to article, DOI:10.1016/j.jclepro.2017.07.126
Publication date:2017
Document VersionPeer reviewed version
Link back to DTU Orbit
Citation (APA):Nimmanterdwong, P., Chalermsinsuwan, B., Østergård, H., & Piumsomboon, P. (2017). Environmentalperformance assessment of Napier grass for bioenergy production. Journal of Cleaner Production, 165, 645-655. https://doi.org/10.1016/j.jclepro.2017.07.126
https://doi.org/10.1016/j.jclepro.2017.07.126https://orbit.dtu.dk/en/publications/edddf1da-9b4c-48c7-ba74-5cb9b45ad48ahttps://doi.org/10.1016/j.jclepro.2017.07.126
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Environmental Performance Assessment of Napier Grass for Bioenergy Production
Prathana Nimmanterdwonga, Benjapon Chalermsinsuwana,b, Hanne Østergårdc,
Pornpote Piumsomboona,b,*
a Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University,
254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand
b Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University,
254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand
c Department of Chemical and Biochemical Engineering, Technical University of Denmark, DTU,
Søltofts Plads 229, 2800 Kgs. Lyngby, Denmark
Abstract
The industrial production of chemicals and energy carriers has grown enormously with the support of new
technologies. A proper assessment is needed to provide broader aspects for long-term sustainability. The
purpose of this study was to evaluate the environmental sustainability of a biorefinery based on
lignocellulosic biomass feedstock using emergy analysis and to propose the method to minimize material
consumption and waste. The concept of emergy is to express the record of all resources used by the
biosphere in earlier steps to produce a product or service, in term of solar energy equivalence. This idea
provides the quantitative indicators involving the resource use and the percent renewability of the systems.
For the proposed biorefinery model, Napier grass (Pennisetum purpureum) grown in Thailand was used as
lignocellulosic feedstock. An emergy assessment was performed in two parts, comprised of the evaluation
of the feedstock cultivation and of a biorefinery producing liquid fuels, methanol, steam, electricity and
other by products, i.e., high purity CO2, sulfur. The emergy results revealed that the bio-based products
depend mostly on non-renewable resources used in both biomass cultivation and biorefinery stages. For
Napier grass cultivation, most of the emergy support came from local resources in term of
evapotranspiration of Napier grass (33%) and the diesel consumption during the cultivation process (21%).
The emergy sustainability indicator of the cultivation was 0.81. The emergy sustainability indicator of the
whole process from cultivation to biorefinery stages dropped to 0.25, since the biorefinery section required
solely economic inputs of which most were non-renewable. In conclusion, the implementation of the
integrated biorefinery concept could minimize material consumption and waste generation and it also has
higher performance in terms of the emergy compared to other existing processes.
Keywords: Emergy assessment, Biorefinery, Napier grass, methanol, combined heat and power
*Corresponding author telephone: +6622187676; e-mail: [email protected]
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1. Introduction
The recent development of biomass utilization systems to reduce the dependence on fossil fuels has been
encouraged to provide effective strategies to achieve a solution to both the economic and environmental
aspects of utilizing non-renewable fossil fuels. Therefore, the ideal biofuels must be drawn from the
feedstock that provides lower greenhouse gas emission than conventional fossil fuels through their life
cycle with less impact on food security.
Using biomass as a feedstock in a biorefinery to convert the biological materials into fuels and chemicals is
still in a nascent state. Industries can either directly use food crops as feedstock or replace existing arable
land for food crops with energy crops which would cause higher food prices and triggers the farmers to
clearing more forest to grow more food crops (Tilman, Socolow et al. 2009). However, using the biomass
from energy crops as feedstocks for a biorefinery requires large area of land to provide sufficient supply.
Alternatively, biomass residues, such as straw, husk and other agricultural co-products or wastes are the
type of promising feedstock for advanced biofuels. Another alternative could be those perennial warm-
season grasses, such as Napier grass (Pennisetum purpureum), Miscanthus (Morandi, Perrin et al. 2016),
Indiangrass and switchgrass (Felix and Tilley 2009). These crops could produce reasonable yields even under
severe conditions on marginal or degraded lands abandoned from agricultural usage with low maintenance
(Campbell, Lobell et al. 2008). Napier grass, which has been widely used to feed local cattle in Thailand and
recently promoted as a bioenergy crop by the Thai government, is studied here as an example of a
lignocellulosic bioenergy feedstock.
An important concern for utilizing biomass as a substitution for primary fuel is that biomass production at
present indirectly involves consumption of non-renewable resources (Giampietro, Ulgiati et al. 1997). The
question arises whether the present bio-based technologies can potentially replace the existing fossil-based
processes in both economic and environmental aspects. Thus, a number of assumptions however have
been made and systematic quantitative information is required to use as a guideline for decision making.
Over the past decades, several tools and methods have been proposed to provide comprehensive criteria
guidance for decision-making, such as techno-economic (Swanson, Platon et al. 2010), life cycle analysis
(Owens 1997), exergy (Dincer and Rosen 2012) and emergy analyses (Odum 1996). The techno-economic
studies provide the economic feasibility aspect whether the production process gives benefits in the range
of the given time. By using feasibility analysis, Fontoura (Fontoura, Brandão et al. 2015) found that
converting an Elephant grass into a biorefinery adds value is economically feasible. However, the value of
the present products is temporary and inverse to real wealth. Thus, the economic benefit does not reflect
long term sustainability. The life cycle analysis (LCA) is a method defined to analyze the environmental
impacts of the production system by focusing on emission throughout the life cycle of the analyzed product.
By using LCA, Chang (Chang, Lin et al. 2017) compared two bioethanol production schemes (using Napier
grass and short rotated Eucalyptus as feedstocks) and the analysis could identify the process that provided
lower environmental impact. Thus, the LCA could provide the guidance for process improvement in the
aspect of environment. However, the LCA analysis does not take the aspect of economics into consideration.
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In the current study, emergy analysis was employed to indicate the sustainability of the proposed bio-based
system. The analysis considers both economic and environmental factors by analyzing all the inputs, both
natural and man-made resources, used to develop a product. The concept of emergy was originally
formulated by H. T. Odum as the amount of available energy of one type (usually solar) that is directly or
indirectly required to produce a product or service (Odum 1996). It includes the amount of free natural
inputs (solar, wind, rain, geothermal, etc.) and economic inputs (materials, man-made energy, and labors) to
the system and is expressed in units of solar equivalent joule (sej).
In our previous literature, the feasibility of two agricultural crops, oil palm and Jatropha, as bioenergy
feedstocks in Thailand was previously evaluated to identify suitable species for energy sources
(Nimmanterdwong, Chalermsinsuwan et al. 2015). By using emergy accounting, it was found that oil palm
required less emergy input per unit biomass and had a higher renewability than Jatropha, i.e., oil palm was
the preferable choice for a biorefinery. Moreover, by using emergy analysis, we could point out that large
portion of human labor required for harvesting and transporting in the biomass cultivation stage. Neglecting
this portion of energy may probably cause a misleading conclusion. The study on cultivating Miscanthus as
energy crop reveals that different logistic strategies affect the emergy used or the environmental cost of
the entire process (Morandi, Perrin et al. 2016).
In recent years, the idea of biorefineries has become promising alternative strategies for industrial
development. To achieve the energy demand and climate change mitigation goals, the idea of extracting
energy from biological materials has been promoted. The facility that converts those materials into fuels,
energy, chemicals and materials is what we called biorefinery (Sengupta and Pike 2012). A variety of
different inputs/feedstocks and conversion technologies can be employed in biorefineries system. The
recent emergy study was done on the bioethanol production in Siena, Italy. It was found that using local
resources (straw and residual geothermal heat) to produce bioethanol provided an appropriate solution for
fossil fuels substitution (Patrizi, Pulselli et al. 2015). Nevertheless, by using emergy assessment, it was also
found that the biorefineries do not completely use renewable resources. Most bioenergy such as bioethanol
(Pereira and Ortega 2010) and biodiesel (Cavalett and Ortega 2010) production processes still require
supplemental non-renewable resources. Our purpose of this study was to evaluate the environmental
sustainability of a biorefinery based on Napier grass as a lignocellulosic biomass feedstock and to minimize
material consumption and waste.
2. Methodology
To achieve sustainable bio-based industries, the biorefinery case studies were designed using Aspen Plus
software to simulate an industrial symbiosis with the closed loop concept of materials and energy through
reuse and recycling. The materials and energy in the process become more optimally used, and the waste
generation is minimized. The system boundary were included the feedstock cultivation, where the data
were obtained from the literature and published surveys in Thailand, and eight production processes of the
biorefinery (Fig. 1) in total of nine processes: (1) cultivation and transportation of Napier grass, (2) gasification,
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(3) combined heat and power plant (CHP), (4) syngas cleaning, (5) fuel synthesis, (6) hydroprocessing (HDP), (7)
methanol synthesis process, (8) carbon dioxide capture and (9) waste water treatment.
2.1 Crop production
Napier grass cultivation data was collected and reported by researchers from Pakchong, Nakhon
Ratchasima province in northeastern of Thailand in year 2013 (DEDE,2013). Napier grass can be harvested 5–
6 times per year. The first harvest takes place four months after planting and ratoons are harvested every
other month for up to seven years. To maintain the crop yield during the 7 years, soil amendments and
harrowing are required after every harvest and weeding are performed twice a year. Other general
assumptions were as follows: (1) local renewable resource information was based on Thailand data
including solar radiation, rain and geothermal which were taken from the Thai Meteorological Department
(2016); (2) evapotranspiration of Napier grass was evaluated using the FAO procedure and the Napier crop
coefficient data from Thai Royal Irrigation Department which was equal to 5.70 mm/d or 2.08 × 107 kg/(ha·y);
(3) average soil loss from crops in Thailand is 25 t/(ha·y) (Pansak, Hilger et al. 2008); (4) organic matter in soil is
1.5% (Norsuwan, Marohn et al. 2014) with the energy content 14.6 GJ/t (Cohen, Brown et al. 2006); (5)
replanting new crops required initial Napier stems about 3,100 - 3,800 kg/ha; (6) initial Napier stems for
cultivation were considered as an external input; (7) diesel fuel consumption rate for Napier grass growing
and harvesting was estimated from data referred from (Morandi, Perrin et al. 2016); (8) for Napier grass
transporting the truck capacity and distance from cultivation field to the plant were approximately 3 tons
per trip and 56 km per trip, respectively. (9) data for all agricultural machinery was obtained from (Morandi,
Perrin et al. 2016); (10) all machines for Napier grass cultivation were assumed to have 20 years lifetime; (11)
the fresh biomass (initial moisture 30%) was sun-dried before transporting to the biorefinery site (after sun-
dried moisture 15%); (12) Napier grass annual production rate (fresh Napier grass) is 70–80 t/(ha·y), which was
hence assumed to be 75 t/(ha·y) (DEDE 2013); (13) the energy content of Napier grass is 18 MJ/kg (Flores,
Urquiaga et al. 2012).
2.1.1 Alternative scenarios for Napier grass crop production
To improve the sustainability of Napier grass cultivation, the dependence on economic inputs could be
reduced by promoting long term productivity with eco-efficient alternatives such as using biofuel driven
machineries, and lower pollution levels on the farm (Maier, Szerencsits et al. 2016). This would provide the
higher utilization of local resources and lower the dependence on external resources (De Jong, Van Ree et
al. 2010). Non-renewable inputs, such as diesel, could be replaced by other fuels, or partially substituted by
renewable inputs, to reduce the reliance on fossil fuels. Some might suggest to use machinery instead of
human labor to reduce the labor emergy input. However, the use of machinery would need to take into
account the indirect labor and fuel consumption. Thus, two scenarios were simulated to predict the
possibility of those proposed suggestions; (1) use tractors for weed removal (higher machinery but lower
direct labor input) and build the biorefinery plant close to the cultivation site (within 10 km distance); (2)
extend scenario (1) by using biodiesel instead of conventional diesel.
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2.2 Description of the biorefinery
The biofuel production was developed using the Aspen Plus 8.6 simulation software. The main objective of
this system is to reduce the dependence of imported inputs. Further, the chosen technologies were (1)
potentially applicable and (2) using continuously regenerated raw materials.
The biorefinery model was simulated to provide 3 main purposes; 1) chemical production which were
methanol and 2 grades of liquid fuels: the naphtha-range (C5-C12) and diesel-range (>C12) qualities, 2) a
combined heat and power plant to generate utilities within the system and; 3) waste treating unit (syngas
cleaning, CO2 capture and waste water treatment) to capture acid gas, treat and recycle water within the
process. Also, the by-product from waste treating units were obtained including concentrated CO2 and
sulfur cake. The details of the whole process were described as follow.
The first process in the biofuel production system was the gasification process, where the biomass is burnt
with air and steam to produce syngas (Preciado, Ortiz-Martinez et al. 2012). The proposed reactor model in
this process was the steam blown dual fluidized bed gasifier, since this is claimed to give a higher efficiency
than a conventional gasifier (Doherty, Reynolds et al. 2013). The syngas outlet stream composed of steam,
H2, CO, CO2 and small amount of H2S. The hot (1,300 oC) gas produced in the gasifier was then sent through
the CHP in the third process to extract the heat from the hot syngas stream. Also, the unconverted gas from
further processes, such as HDP and methanol synthesis, was recovered back into the CHP where the gas
and air combusted to provide more heat to the system. The steam that was generated, which carried a
large amount of energy, was sent to the steam turbine to produce electricity. In this process, electricity and
heat were produced simultaneously. Besides power, the CHP process produced steam under four
conditions to support the whole system. These were medium temperature steam (250 oC, 2.5 MPa), high
temperature steam (500 oC, 2.5 MPa), medium pressure steam (200 oC, 2.8 MPa) and high pressure steam
(510 oC, 6.2 MPa). The heat and power generated were primarily used within the system, while the
remaining were considered as external products including; 7.9 MW electricity and 3.72 x 108 MJ high
pressure steam/y.
After the CHP process, the cold syngas stream (180 oC) went through the outlet to the gas cleaning process.
In this process, the cold syngas was sent to the water scrubber to remove small particulates, such as fly ash,
and was then delivered to the sour water-gas shift reactor to adjust the CO: H2 ratio at 2.1. The sour gas was
then sent to the monoethanolamine (MEA) absorber to remove the acid gases (including CO2 and SO2). The
cleaned syngas was then fed into the fourth stage wherein the refined gas was synthesized to liquid fuels
through the Fischer-Tropsch process. The reactor was operated at 200 oC and 2.5 MPa, based on the NREL
literature model (Swanson, Platon et al. 2010). After the Fischer-Tropsch reaction, to obtain the liquid fuels
with a high gasoline portion, the liquid product was treated with H2 in HDP. Finally, the liquid fuels within
the naphtha-range (C5-C12) and diesel-range (>C12) qualities were obtained. The remaining unconverted
syngas from the fuel synthesis was sent to the methanol synthesis process to produce methanol as a by-
product. The methanol synthesis was developed using kinetic reaction model referred from De María study
(De María, Díaz et al. 2013).
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The assumptions for treatment of the wastes were based on the following literatures: (1) water condensate
from the syngas production, which contained soluble volatile matter at less than 0.02% by mass and was
treated and recycled to the CHP process for steam production. The emergy calculation data referred from
Arbault (Arbault, Rugani et al. 2013). (2) the flue gas from the CHP process which contained CO2 at about 980
ppm was sent to the treating unit for carbon capture process where a high CO2 concentration was obtained
as a by-product. The system referred from (Desideri and Antonelli 2014) where the amine absorption was
employed. Also some economic information for emergy accounting referred from Singh (Singh, Croiset et al.
2003). No detailed simulation was made for the waste treatment processes.
2.3 Equations and notations for emergy accounting
The energy systems diagram of the system is shown in Fig. 1. To complete the emergy accounting, the
amount of emergy input to the system was calculated by multiplying the raw data inputs (as mass flow,
energy flow, etc.) with the emergy conversion factor (Unit Emergy Value; UEV), which was obtained from
previous studies. The UEV indicates the amount of emergy required to produce a unit of product(s),
expressed as sej/J, sej/kg or sej/L of product. A higher UEV value means a larger amount of emergy input is
required for the process to obtain the product(s). When comparing products or processes, the UEV can be
used to reveal the resource use efficiency of the system, where the product with a lower UEV has a higher
production efficiency (Bastianoni and Marchettini 1996).
Typically, the emergy input of the systems can be classified as free natural resources available within
system boundary or economic inputs. Free natural resources are separated into renewable resources (R)
and non-renewable resources (N). For example, renewable resources include solar radiation, wind and rain,
while non-renewable resources include minerals and soil. Only the largest emergy flow among solar
radiation, wind and rain is counted in order to avoid double counting of renewable resource (Odum 1996).
Economic inputs can be classified into two types, i.e., purchased energy and materials (F) and human labor
(L). Labor may contribute directly (DL) or indirectly (IL) as services. An important emergy input to an
agricultural production system is labor (labor directly applied to the process; DL) and services (external labor
coming from the economic sector or larger scale outside the system boundary; IL). The economic inputs can
be classified into renewable parts (FR + LR) and non-renewable parts (FN + LN) where F = FN + FR and L = LN + LR.
The emergy input from source i is defined as Emi and the available energy of the product j is defined as Enj.
In Eqs. (1) – (5), different emergy indicators are defined with respect to the four variables; R, N, F and L.The
descriptions of each notation are summarized in Table 1.
UEV of the product(s), τ = ∑ Emi
ni=1
∑ Enini=1
(1)
Global Renewability, %Rglobal = R+FR+LR
R+N+F+L (2)
Environmental Loading Ratio, ELR = N+F+L
R (3)
Emergy Yield Ratio, EYR = R+N+F+L
F+L (4)
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Emergy Sustainability Index, ESI = EYR
ELR (5)
The global renewability in equation (2), %Rglobal, is the indicator that is used to identify the fraction of
resources used that comes from global renewable resources. While ELR, in equation (3), is the ratio of local
non-renewable and economic inputs emergy to local renewable emergy, which implies the ecosystem
stress due to the processes within the system boundary. The value reflects the renewable fraction of the
system in a different way from %Rglobal. The ELR value indicates only the locally renewable resources that
support the system while the %Rglobal also counts the renewable fraction from economic inputs.
The emergy yield ratio (EYR), in equation (4), is the ratio of the total emergy that drives the system to the
economic inputs emergy, and measures the ability of the system to exploit the local resources. The value
should be much higher than 1 otherwise the process will act as a consumer rather than a producer. Finally,
the ratio between ELR and EYR is presented as ESI in equation (5), the emergy sustainability indicator (ESI). In
Eqs. (1) – (5), the sustainability in the ESI indicator was defined with respect to the four variables; R, N, F and
L. The lowest possible value of ESI is zero. ESI value close to zero indicates the process produces negative
yield to the society and creates large burden to environment. In the other hand, those greater than one
indicates that the process has high contribution to the economy without creating heavily loads to its
environment (Brown and Ulgiati 2004).
In this study, Napier plantation requires initial Napier stem to plant the crop that lasts for 7 years. Within
this period of time, the UEV of output (Napier grass biomass) and input (initial Napier stem) were assumed
to be equal. Iteration is often applied to deal with this issue (Morandi, Perrin et al. 2016), but the procedure
employed in this study was derived from the mathematical formula described below studying equation (6).
Let τNapier be the UEV of the Napier grass biomass; Em0, Em Napier,in and Em Napier,out be the emergy flow of all inputs (except initial Napier stem), initial Napier stem and total emergy flow to the Napier grass biomass,
respectively; and MNapier,in and MNapier,out be the amount of initial Napier stem and Napier grass biomass,
respectively. The emergy accounted to the output will be equal to the summation of all the inputs including
the initial Napier stem (Em Napier,out = Em0 + Em Napier,in = Em0 + MNapier,inτNapier), while the total emergy to the output will equal the multiplication between the raw amount of product (MNapier,out) and UEV of the product
(τNapier). Thus, equation (6) was obtained.
EmNapier,out = Em0+MNapier,inτNapier = MNapier,outτNapier
Napier grass UEV,τNapier =Em0
(MNapier,out - MNapier,in) (6)
2.4 Emergy analysis of co-production process
In emergy analysis, total emergy driving the process allocates to each of the products equally (Brown and
Herendeen 1996). While this rule is applied, it is important to understand that one-product systems and
multi-product systems cannot directly compare. When products cannot be produced independently in the
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process, the emergy allocated to each product from that process is equal to the total emergy inputs.
According to the procedure, most multi-product systems often rely on higher emergy than one-product
system. Since they carried the emergy of the whole production process. For example, the combine heat and
power process which produces electricity and steam as by-product has UEV of 1.20 × 105 sej/Jelectricity (Sha and
Hurme 2012). While solar power generates the electricity with UEV only 8.92 × 104 sej/Jelectricity (Paoli, Vassallo
et al. 2008). It may lead to misinterpretation if we compare the emergy of these products that were
generated from these two processes.
Since the biorefinery system in this study has more than one product, joint and weighted average indicators
were used (Bastianoni and Marchettini 2000). The joint production process is defined as the system that
produces co-products simultaneously (Figure 2a). The joint UEV (τjoint) was calculated by equation (7).
The weighted average indicators can be evaluated from the weighted emergy fraction by the energy
contents of the products that have the same quantity as the joint-production products but produced by two
or more independent ways (Figure 2b). The weighted average UEV (τave) was evaluated using equation (8). The same procedure can be applied to other indicators, such as the EYR or ELR.
Joint UEV, intbrf
jom p s f
Em
En En En Enτ =
+ + + (7)
Weighted average UEV, pm
avg m pm p s f m p s f
EnEn
En En En En En En En Enτ τ τ= +
+ + + + + +
fs
s fm p s f m p s f
EnEn
En En En En En En En Enτ τ+ +
+ + + + + + (8)
where Enm, Enp, Ens and Enf are the energy flow of methanol, electricity, steam and liquid fuels,
respectively, from the biorefinery; τm, τp, τs and τf are the UEVs of methanol, electricity, steam and liquid fuels, respectively from independent production systems; Embrf is the total emergy input to the biorefinery
system; and Emm, Emp, Ems and Emf are the total emergy input to the methanol, electricity, steam and
liquid fuels independent production system, respectively.
2.5 Human labor in emergy accounting
In emergy analysis, there are different procedures to include human resources into the emergy accounting.
One of the conventional procedures is to use the emergy to money ratio (EMR) as the UEV for labor inputs
in monetary units, where EMR is an indicator that expresses the quantity of emergy that supports the
monetary value of the production or GDP of the country where the production takes place. It is measured in
sej/US$ or another relevant currency. For example, the global average EMR can be obtained by dividing the
global emergy budget of 1.05 × 1026 sej/y by the global money flow of 6.06 × 1013 US$/y (Kamp, Morandi et al.
2016) to give an UEV as emergy per monetary value of 1.73 × 1012 sej/US$.
Another approach for human labor accounting is to allocate the emergy budget per hour worked to each
category using specific parameters. The refined method has been applied from that of (Kamp, Morandi et al.
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2016) considering the data for three production sectors in Thailand (agricultural, industry and services) for
the year 2008 (Aemkulwat 2010). The assumption involved is using a money-based distribution to indicate
the emergy shared to people working in different levels of the production process. In Thailand, the emergy
budget of 3.20 × 1024 sej/y in 2008 was distributed across the labor system based on %GDP distribution as
follows; agriculture sector 8.8% (2.82 × 1023 sej/y), industrial sector 48% (1.54 × 1024 sej/y) and services 43.2%
(1.38 × 1024 sej/y) (Aemkulwat 2010). The hours worked by each production sector were calculated using the
average working hours and population of people working in each production sector, where the hours
worked by people in the agricultural, industrial and service sectors were 3.11 × 1010 h/y, 1.71 × 1010 h/y and
3.29 × 1010 h/y, respectively. The ratios between the emergy distribution and hours worked by each sector
gave UEVs for people working in the agricultural, industrial and service sectors of 9.06 × 1012 sej/h, 8.99 ×
1013 sej/h and 4.20 × 1013 sej/h, respectively, as fully described in the supplementary material (note 1.11).
Using EMR as human labor UEV or using man-hour UEV has distinct different advantages. The EMR is the
expression of the average value of the whole nation, while the man-hour UEV is attempted to be the
specific value of a sector sorted by level of income. In this study, both methods are applied. The direct labor
input is considered a domestic labor, using man-hour UEV based on Thailand. Only the agricultural and
industrial sectors were considered, since the labor involved farmers and industrial operators. For the
indirect labor, which corresponded to external labor, the global average EMR was used as the labor UEV.
The summary for labor UEV used in this study is shown in Table 2.
3. Results and discussion
Figure 1 depicts the process which is under investigation as described earlier. It is mainly composed of
biomass cultivation section and biorefinery section. The details of emergy accounting for each section are
tabulated in Table A1 – A8 as shown in the supplementary material. Also, the resource distributions to each
production process is summarized in Table A9, where the emergy inputs to each process are categorized
into R, N, F and L as mentioned in section 2.3.
3.1 Emergy analysis of biomass cultivation
The obtained emergy analysis of this study for Napier grass cultivation was compared with that previously
reported in Table 3 to indicate the potential of Napier grass from Thailand as a lignocellulosic bioenergy
crop. In addition to Napier grass, waste from palm oil production is also a potential bioenergy feedstock in
Thailand. Comparison between Napier grass and palm cultivation revealed that the UEV of Napier grass was
lower than that for palm oil which means that less resources are used to produce one joule of biomass.
However, the global renewable fraction was lower which is not desirable. The major sources of emergy
inputs to Napier cultivation were evapotranspiration which reflects the amount of water absorbed by
Napier grass from natural resources followed by diesel consumption at 33% and 30% of the total emergy
input, respectively. The direct on site labor had a high impact on the cultivation processes, due to it being
rural farming. Since the direct labor is on-site labor, the UEV of Thailand was used to calculate the direct
labor emergy input in this process. Since the renewability fraction in human resources in Thailand
accounted for only 10%, a high direct labor input to the Napier grass cultivation process caused a low
renewability to the biomass product (NEAD, 2010). The present result showed that almost 60% of resources
consumed were non-renewable, causing a high load to the environment, as presented in the emergy
indicator values. However, the EYR for Napier grass cultivation was 1.53 (higher than 1), which means the
process acts as a producer more than a consumer. Nevertheless, the EYR of Napier grass was still lower than
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miscanthus, switchgrass and sugarcane, since the process required much more imported and human
resources as shown in Table 3. The ELR of Napier cultivation was 1.89, which lies within the moderate
impact to the environment range, according to (Brown and Ulgiati 2004). Due to the lower renewability of
the process, Napier grass cultivation generated the higher environmental load than miscanthus and
sugarcane. Finally, the ESI of Napier grass cultivation, which indicated the sustainability of the process from
the perspective of EYR and ELR, was one of the suitable candidate compared to the alternative biomasses
reported in Table 3.
It is important to note that the input of labor may be calculated in different ways and includes more or less
indirect labor. For comparison, when evaluated without labor 51% global renewability of Napier grass
cultivation can be obtained with a lower ELR (1.00), a higher EYR (2.00) and ESI (2.00). However, comparison
of the resource use for the cultivation of biomass by collating UEVs from different studies may be
misleading due to the different assumptions and contexts of each study. For example, some literature
values did not consider the indirect labor (Coppola, Bastianoni et al. 2009, Pereira and Ortega 2010,
Morandi, Perrin et al. 2016), some did not take into account the renewability of the economic inputs (Lin
and Sagisaka 2012) and some did not describe their assumptions relating to labor accounting, which was
the main emergy input into their system (Goh and Lee 2010, Pereira and Ortega 2010). For those reasons,
recalculation on the same basis is required as attempted in Table 3.
To improve the sustainability of Napier grass cultivation, two scenarios were simulated to predict the
possibility of those proposed suggestions (described in Section 2.1.1). The results (Table 4) revealed an
improved process in many aspects. For the first case, the UEV was reduced 1.45-fold to 9.30 × 103 sej/J,
%Rglobal increased 1.4-fold to 55%, EYR increased 1.31-fold to 2.01, ELR was reduced 1.91-fold to 0.99 and the
ESI was improved 1.95-fold to 2.04. In the second case, using biodiesel instead of conventional diesel fuel did
not improve %Rglobal, since the biodiesel production process was highly dependent on external resources and
most were non-renewable resources (17%) (Nimmanterdwong, Chalermsinsuwan et al. 2015). For this reason,
the ESI of this scenario in Table 4 also shows that, with current biodiesel production process, the
substitution of diesel with biodiesel is not a good alternative (higher UEV but higher %Rglobal). Thus, we
propose model (1) over model (2), since it has a higher sustainability indicator and in addition, a lower UEV.
3.2 Emergy analysis of the Napier-based biorefinery
The Napier-based biorefinery system in this study was modeled based on technologies as previously
mentioned that operated using material and energy carriers produced within the system as the first priority.
In this way, we reduced the dependence of economic inputs and the system acted as partly self-sufficient.
The UEV of the biorefinery system was calculated as a joint UEV of four products, electricity, steam, liquid
fuels (products from fuel synthesis process) and methanol, according to equation (7). The other products,
such as ash, concentrated CO2 and sulfur cake, were considered as by-products and not taken into account
since the references used to compare with our study might also have by-products that could not be directly
compared with our case. Also, these by-products accounted for only small amount of the energy among all
output products.
The heat and power generated from the biorefinery can reduce the amount of economic inputs by 3.45 ×
1010 J/y (steam) and 8.40 × 109 J/y (electricity), which accounted for 4.25 × 1019 sej/y (assuming that steam and
electricity were imported from a biomass CHP process outside the system boundary). In addition, the
wastewater treatment unit provided recycled water to the system that could reduce the otherwise high
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amount of fresh water input to the system by almost 20 t/h accounting for 1.56 × 1017 sej/y. Therefore, by
using the material and energy integration concept, a 27% lower emergy consumption was obtained.
An overview of the emergy profile (Figure 3) shows that the cultivation stage dominates the emergy
consumption of the entire process, followed by the chemical production processes (including gasification,
fuel synthesis, HDP and methanol synthesis), waste treatment units and CHP accounting for 44%, 30%, 23%
and 3% of the total, respectively. The emergy allocated to each process is summarized in Figure 4, where the
highest resource consumption in the biorefinery was syngas cleaning and fuel synthesis (Table A9). The main
input to fuel synthesis was purchased resources from cobalt catalyst (6%). Makeup MEA, accounted for 14%
of total emergy input to the biorefinery, fed to gas cleaning process to extract the low concentration of CO2
produced from gasification process.
From Table 5, the UEV for the biorefinery, which was considered as a joint production system (Figure 2a)
was 3.31 × 104 sej/J and 1.96 × 104 sej/J when including and not including the labor, respectively. The UEV
indicates that about 33 thousand solar emergy was required to produce a joule of products from this
biorefinery system. As the diversity of products created by the system has a different ability to do work, the
joint UEV may not be an appropriate value to apply in further studies. On the other hand, it can be used to
compare the biorefinery with single processes that produce an equal quantity and quality of the same
products, as previously suggested (Bastianoni and Marchettini 2000). These authors defined a weighted
average UEV (Equation (8)), where the UEV of the product is obtained from an independent process (Figure
2b).
A number of specific alternative ways to produce electricity, steam, liquid fuels and alcohol are presented
in Table 6. Among the given options, the best route to produce the target products (the lowest weighted
average UEV) is by producing methanol from willow, heat and power from biomass CHP, additional power
from wind power (since the power to heat ratio of biomass CHP is insufficient) and bio-diesel from rapeseed.
In the best scenario, the weighted average UEV is 2.9 times higher than the present study. Meaning that,
the biorefinery has utilized, in emergy terms, the resources more efficiently than that of the existing
independent production process.
The emergy indicators of the biorefinery, including global renewability, EYR, ELR and ESI, were calculated
under the simulation information obtained from theoretical assumptions. Global renewability of the
biorefinery system accounted for 23% of the total emergy input, which is quite low due to large fraction of
non-renewable materials consumed by the agricultural process and most were consumed by the bio-based
production system, such as makeup MEA for syngas cleaning and cobalt catalyst for fuel synthesis (data
reported in Table A1-A7 supplementary material). While most resources consumed are external resources
leading to lower EYR value (from 1.53 in cultivation phase to 1.21). Due to the low EYR from the upstream
production system, an industrial system that always demands import resources as the main input would
continuously lower the EYR value and increase the ELR value (accounted for 14%) of the whole system since
the import resources are considered as the parameter that caused the principal load to the environment.
Finally, the obtained ESI of the Napier-based biorefinery was 0.25. In addition, to achieve higher
sustainability, the optimization of chemical consumption is required. From emergy analysis, we found that it
is possible to obtain ESI up to 10 percent if 50 percent of MEA can be recovered in gas cleaning process.
4. Conclusion and recommendations
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The emergy methodology for sustainability assessment has advantages in that it can reveal the importance
of free environmental services and resources. In this study, emergy assessment was used as a tool for
evaluating biorefinery based on biomass resources, and provided insight into the evaluated system in terms
of environmental impacts and used resource efficiency in producing the outputs. The values will show
whether the evaluated system is optimally employed. From the results, some limitations in emergy analysis
was found. Due to globalization, societies utilize resources globally. For example, electricity might be
imported from neighboring countries that is produced by wind or hydropower. According to the
conventional definition of ESI, only the local renewable emergy is counted in the renewable fraction for
calculating the sustainable index. Thus, to be more accurate, the global renewable resources should be
recognized in the ESI. This would provide broader perspective to the sustainability of the processes which
required a large portion of external inputs but were partially renewable.
The overall results revealed that the bio-based products are not a completely renewable. They depend
mostly on non-renewable resources in both biomass cultivation and biorefinery stages. In the Napier grass
cultivation process, the dominating emergy input is evapotranspiration, diesel consumption and human
labor, respectively. Eventhough there was no agreement for human labor accounting methods, different
assumptions among present emergy literatures have been made. Therefore, it is important to perform
emergy evaluation either with or without human labor input to clarify the range of product UEV for the
further studies.
The diesel fuel for biomass cultivation and transportation also dominated the ESI. Additionally, replacing this
fossil fuel with alternative fuels cannot directly solve the problem. It would put our situation into a dilemma
regarding the high indirect fossil fuel consumption behind the production process.
In most industrial production processes, all emergy inputs other than biomass are considered as external
resources (except for some cases for example; geothermal power plant, wind power plant, etc.). Those are
often produced from non-renewable resources, which made the system to have a low EYR and to create a
high burden to the environment. Even our proposed biorefinery model, that attempts to promote the
sustainability of the existing system, can achieve a higher efficiency in terms of resources utilization than
the individual production systems currently in existence. Nonetheless, the ESI of the whole system is still
too low and require further improvement. As suggested in the green engineering concept (Allenby and
Richards 1994), besides maximize resource efficiency, renewable resources should replace non-renewable
ones as much as possible.
Acknowledgements
The authors would like to thank the Department of Chemical Technology, Faculty of Science, Chulalongkorn
University and Department of Chemical and Biochemical Engineering, Technical University of Denmark for
all supports on this study. Also, this study was financially supported by the Thailand Research Fund
(IRG5780001) and the Graduate School of Chulalongkorn University.
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Figure captions
Fig. 1 Flow diagram of the system including biomass cultivation and biorefinery.
Fig. 2 Alternative pathways to produce the target products. (a) The joint production system and
(b) the alternative independent production system.
Fig. 3 Emergy profile for the system including biomass cultivation and biorefinery.
Fig. 4 Emergy profile for each process from field to biorefinery plant gate.
Tables
Table 1 Description of abbreviations and notations used in equations.
Table 2 The human labor UEVs used in this study.
Table 3 Emergy assessment of cultivation of different lignocellulosic biomass as feedstock.
Table 4 Two alternative models for improving Napier grass cultivation in Thailand.
Table 5 Emergy indicators of the system from cultivation to biorefinery.
Table 6 Alternative pathways to produce the target products.
Table A1 Emergy analysis of napier grass plantation.
Table A2 Emergy analysis of biomass gasification process.
Table A3 Emergy analysis of CHP process.
Table A4 Emergy analysis of syngas cleaning process.
Table A5 Emergy analysis of fuel synthesis process.
Table A6 Emergy analysis of hydroprocessing.
Table A7 Emergy analysis of methanol synthesis process.
Table A8 Emergy analysis of waste treating process.
Table A9 Resources distributions to each production process.
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Table A10 Lists of UEVs used in this study (the values are based on the 1.20×1025 sej/y baseline (Brown,
Protano et al. 2011)).
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Table 1 Description of abbreviations and notations used in equations.
Abbreviations/
Notations Descriptions
UEV Unit emergy value
sej Solar equivalent joule
LCA Life cycle analysis
CHP Combined heat and power plant
HDP Hydroprocessing
EMR Emergy to money ratio
equation (1)
Emi Emergy of source i (sej/y)
Enj Energy flow of product j (J/y)
τ Unit emergy value (sej/J)
equations (2) - (5)
%Rglobal Global renewability
ELR Environmental loading ratio
EYR Emergy yield ratio
ESI Emergy sustainability index
R Free natural resources as renewable resources
FR Renewable portion of purchased energy and materials (excluded human labor)
LR Renewable portion of human labor
N Free natural resources as non-renewable resources
F Purchased energy and materials (excluded human labor)
L Human labor
equations (6)
Em Napier,out Total emergy flow to the Napier grass biomass
Em0 Emergy flow of all inputs (except initial Napier stem)
Em Napier,in Emergy flow of initial Napier stem
MNapier,in The amount of initial Napier stem as input stream
MNapier,out The amount of initial Napier grass biomass as product stream
τNapier Unit emergy value of Napier grass (sej/J)
equations (7) and (8)
τjoint Unit emergy value of the products from joint production system (sej/J)
Embrf Total emergy flow to the biorefinery (sej/y)
Enm Energy flow of methanol product (J/y)
Enp Energy flow of electricity generated (J/y)
Ens Energy flow of steam product (J/y)
Enf Energy flow of liquid fuels product (J/y)
τavg Unit emergy value of the products from independent production system (sej/J)
τm Unit emergy value of methanol produced independently (sej/J)
τp Unit emergy value of electricity produced independently (sej/J)
τs Unit emergy value of steam produced independently (sej/J)
τf Unit emergy value of liquid fuels produced independently (sej/J)
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Table 2 The human labor UEVs used in this study.
Level UEV Unit Supplementary material
Direct labor (DL) Agricultural sector
Industrial sector
9.06×1012
8.99×1013
sej/h
sej/h Note 1.11
Indirect labor (IL) Global average 1.73×1012 sej/$ Note 1.12
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Table 3 Emergy assessment of cultivation of different lignocellulosic biomass as feedstock.
Remarks: the results have been recalculated based on previous literatures data.
- These missing numbers could not track from the reference literatures.
a Jatropha and oil palm production in Thailand, with extracted oil and produced residues as a by-product.
b Case LO (organic management in loamy soil) was selected.
c Palm production in Malaysia, with extracted oil and produced residues as a by-product.
d Rice biomass includes rice grain, rice straw and chaff.
Biomass UEV (sej/J) Local resources
Imported
resources Human labor %Rglobal EYR ELR ESI Ref.
R N FR FN DLR DLN ILR ILN
Energy-crops
Napier grass (with L)
Napier grass (w/o L)
1.35×104
9.35×103
35%
50%
0%
0%
1%
1%
34%
49%
2%
19%
1%
8%
39%
51%
1.53
2.00
1.89
1.00
0.81
2.00 This study
Miscanthus (with L)
Miscanthus (w/o L)
1.42×104
1.42×104
95%
96%
2%
2%
1%
1%
2%
2%
0%
0%
-
-
96%
96%
37.7
42.1
0.05
0.05
754
895
(Morandi, Perrin et
al. 2016)
Switchgrass (with L)
Switchgrass (w/o L)
2.12×104
1.72×104
32%
40%
3%
4%
1%
0%
45%
56%
1%
8%
1%
9%
37%
40%
1.55
1.77
2.11
1.53
0.73
1.16 (Felix and Tilley 2009)
Jatrophaa (with L)
Jatrophaa (w/o L)
1.95×105
3.16×104
6%
35%
3%
21%
0%
1%
6%
43%
8%
70%
1%
6%
23%
36%
1.10
2.27
16.56
1.85
0.07
1.23
(Nimmanterdwong,
Chalermsinsuwan et
al. 2015)
Food-crops biomass/
Wheat strawb (with L)
Wheat strawb (w/o L)
1.16×105
1.15×105
19%
19%
1%
1%
14%
14%
65%
66%
0%
1%
-
-
33%
33%
1.26
1.26
4.21
4.16
0.30
0.30
(Coppola, Bastianoni
et al. 2009)
Sugarcane (with L)
Sugarcane (w/o L)
2.79×104
2.12×104
35%
47%
7%
9%
1%
2%
33%
43%
4%
20%
-
-
44%
48%
1.72
2.24
1.83
1.15
0.94
1.96
(Coppola, Bastianoni
et al. 2009)
Oil palma (with L)
Oil palma (w/o L)
6.94×104
2.54×104
15%
41%
9%
25%
0%
0%
12%
34%
4%
33%
3%
24%
28%
41%
1.32
2.92
5.64
1.43
0.23
2.05
(Nimmanterdwong,
Chalermsinsuwan et
al. 2015)
Palmc (with L)
Palmc (w/o L)
3.26×104
1.21×104
26%
69%
3%
8%
0%
0%
9%
23%
10%
28%
7%
19%
58%
69%
1.40
4.32
2.90
0.44
0.48
9.72 (Goh and Lee 2010)
Rice biomassd (with L)
Rice biomassd (w/o L)
1.45×105
9.03×104
8%
12%
0%
0%
-
-
55%
87%
-
0%
-
37%
8%
12%
1.08
1.14
11.91
7.04
0.09
0.16
(Lin and Sagisaka
2012)
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Table 4 Two alternative models for improving Napier grass cultivation in Thailand.
Base case Proposed
model 1*
Proposed
model 2**
Total emergy flow (sej/y) 6.39×1019 4.40×1019 4.81×1019
Local resources (R+N), % from total 35% 50% 46%
Resources from outside (F), % from total 35% 25% 31%
Labor (direct & indirect), % from total 31% 25% 23%
UEV (sej/J) 1.35×104 9.30×103 1.02×104
%Rglobal 39% 55% 55%
EYR = (R + N + F + L) / (F + L) 1.53 2.01 1.86
ELR = (N + F + L) / R 1.89 0.99 1.17
ESI = EYR / ELR 0.81 2.04 1.58
* Using tractors for weed removal, build biorefinery plant close (< 10 km) to the cultivation site
** Using biodiesel instead of diesel, tractors for weed removal, build biorefinery plant close (< 10 km) to the cultivation
site
Table 5 Emergy indicators of the system from cultivation to biorefinery.
Item Unit
Joint UEV of the products with L 3.31×104 sej/J
Joint UEV of the products without L 1.96×104 sej/J
Global Renewability (%Rglobal) 23%
EYR of the system = (R + N + F + L) / (F + L) 1.21
ELR of the system = (N + F + L) / R 4.88
ESI of the system = EYR / ELR 0.25
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Table 6 Alternative pathways to produce the target products.
Country
source UEV (sej/J) Ref.
Bio-alcohol
Methanol from willow Sweden 6.06×104 (Cavalett and Rydberg 2011)
Methanol from wood Italy 2.66×105 (Pimentel and Patzek 2008)
Steam and power co-production
Biomass CHP process 1 Finland 1.62×104 (Sha, Losowska et al. 2011)
Biomass CHP process 2 Finland 3.44×104 (Sha and Hurme 2012)
Biomass CHP process 3 Denmark 2.31×105 (Kamp and Østergård 2013)
Power
Wood power plant USA 6.72×104 (Odum 1996)
CSP powerplant China 6.39×104 (Zhang, Wang et al. 2012)
Solar power plant Italy 8.92×104 (Paoli, Vassallo et al. 2008)
Wind power 1 China 1.74×104 (Yang, Chen et al. 2013)
Wind power 2 Italy 6.21×104 (Brown and Ulgiati 2002)
Geothermal power plant Italy 1.47×105 (Brown and Ulgiati 2002)
Hydro power plant 1 Italy 6.23×104 (Brown and Ulgiati 2002)
Hydro power plant 2 Tibet 1.56×105 (Zhang, Pang et al. 2016)
Hydro power plant 3 Brazil 8.28×104 (Tassinari, Bonilla et al. 2016)
Liquid fuels
Macroalgae oil 1 Italy 2.64×107 (Bastianoni, Coppola et al. 2008)
Macroalgae oil 2 Brazil 3.51×105 (da Cruz and do Nascimento 2012)
Bio-diesel from soy bean Brazil 3.90×105 (Cavalett and Ortega 2010)
Bio-diesel from palm oil Thailand 2.14×105
(Nimmanterdwong,
Chalermsinsuwan et al. 2015)
Weighted average UEV of the
best scenario*
9.57×104
*Methanol from willow, heat and power from biomass CHP process 1, additional power from Chinese wind
power and bio-diesel from palm oil
CSP = concentrating solar power
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Fig. 1 Flow diagram of the system including biomass cultivation and biorefinery.
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Fig. 2 Alternative pathways to produce the target products: (a) the joint production system and
(b) the alternative independent production system.
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Fig. 3 Emergy profile for the system including biomass cultivation and biorefinery.
44%
30%
3%
23%
Cultivation
Chemical production
CHP
Waste treating units
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Fig. 4 Emergy profile for each process from field to biorefinery plant gate.
0.00E+00
1.00E+19
2.00E+19
3.00E+19
4.00E+19
5.00E+19
6.00E+19
Cultivation
(B100)
Gasification
(A100)
CHP (A200) Syngas
clean.
(A300)
Fuel syn.
(A400)
HDP (A500) Methanol
syn. (A600)
CO2
capture
(A700)
Waste
water treat.
(A800)
Em
erg
y f
low
(se
j/y
)
R N FR FN LR LN
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Environmental Performance Assessment of Napier Grass for Bioenergy Production
Prathana Nimmanterdwonga, Benjapon Chalermsinsuwan
a,b, Hanne Østergård
c,
Pornpote Piumsomboona,b,*
a Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn
University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand
b Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University,
254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand
c Department of Chemical and Biochemical Engineering, Technical University of Denmark, DTU,
Søltofts Plads 229, 2800 Kgs. Lyngby, Denmark
Highlights
- Emergy analysis is employed to biomass cultivation and biorefinery processes.
- Heavy consumption of non-renewable resources in Napier grass cultivation was found.
- The proposed biorefinery can achieve a higher emergy performance than existing ones.
- The proposed biorefinery consists of material and energy through reuse and recycling.
- Human labor and renewable resource accounting was discussed.